In communication channels, and particularly in magnetic recording channels, a Viterbi algorithm is often used for sequence detection in channel detectors due to the presence of inter-symbol interference (ISI). The term “inter-symbol interference” refers to the scenario when the signal pulses or symbols spread into adjacent pulses or symbols in a time domain. The Viterbi algorithm is a maximum likelihood detection algorithm, and it is optimal when additive white Gaussian noise constitutes the only noise source.
In practical recording channels, however, transition jitter noise is one of the primary impediments to reliable data recovery, particularly for ultra-high linear density recording. Transition jitter noise refers to random shifts of individual transition boundaries between different magnetization states in the recording media relative to an expected transition location within a bit interval of a channel detector.
Channel detectors typically lack a-priori knowledge of the random shifts. Consequently, the channel detectors assume that bit transitions occur in a nominal location. Mismatches between the read back signal and the assumed bit transition locations give rise to the jitter noise. Since jitter noise is random, conventional detectors cannot easily be tuned to the exact random shift.
Traditional partial response maximum likelihood (PRML) detectors ignore the nature of jitter noise, and treat jitter noise in the same way as additive white Gaussian noise, resulting in significant performance loss. Transition jitter is typically both correlated and data dependent. Treating the jitter noise as additive noise, it is possible to model the combination of jitter and electronic noise with an auto-regressive model, such that the correlation structure (deemed data dependent) can be taken into account in the branch metric calculation using a trellis survivor path competition. The success of this method depends on how close the auto-regressive model can approximate the actual jitter/electronics noise. However, since the noise resulting from the transition jitter is not strictly an auto-regressive (or Markov) process, modeling the jitter noise using a finite order Markov model is less than desirable.
There is an ongoing need in the art for improved channel detection systems and methods in the presence of transition jitter noise. Embodiments of the present invention address these problems and provide advantages over existing systems.